library(maps)
library(mapdata)
library(sidrar)
library(geobr)
library(ggplot2)
library(sf)
library(rio)
library(vctrs)
library(dplyr)
install_formats()
library(sidrar)
library(knitr)
knitr::opts_chunk$set(echo = TRUE)
#Plot the data in a visually appealling way
library(RColorBrewer)
library(classInt)
library(tibble)
#for interactive maps
library(tmap)
library(leaflet)
library(plotly)
library(leaflet.extras)
library(htmlwidgets)
library(htmltools)
Testing geobr package
#Municipality of São José dos Campos
muni <- read_municipality(code_muni=3549904, year=2019 )
no_axis <- theme(axis.title=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank())
ggplot() +
geom_sf(data=muni, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
labs(subtitle="São José dos Campos", size=8) +
theme_void() +
no_axis
#Neightboorhoods in São José dos Campos
sp_census <- read_census_tract(code_tract="SP", year=2010)
sjc_census <- sp_census[which(sp_census$name_muni=='São José Dos Campos'),]
ggplot() +
geom_sf(data=sjc_census, fill="#2D3E50", color="#FEBF57", size=.15, show.legend = FALSE) +
labs(subtitle="São José dos Campos", size=8) +
theme_void() +
no_axis
sp <- read_municipality(code_muni="SP", year=2010)
no_axis <- theme(axis.title=element_blank(),
axis.text=element_blank(),
axis.ticks=element_blank())
ggplot(data=sp) +
geom_sf(aes(fill="b"), size=.15, show.legend = FALSE) +
labs(subtitle="São Paulo", size=8) +
theme_void() +
no_axis
Maps, Coronavirus and Population
coronadata <- read.csv2("INFLUD-07-09-2020.csv",
header = TRUE,
stringsAsFactors=TRUE,
na = "NA",
dec = ".",
sep=";")
pop <- read.csv2("estimativa_dou_2015_20150915.csv",
header = TRUE,
stringsAsFactors=TRUE,
na = "NA",
dec = ".",
sep=";")
levels(coronadata$SG_UF_NOT)
## [1] "AC" "AL" "AM" "AP" "BA" "CE" "DF" "ES" "GO" "MA" "MG" "MS" "MT" "PA"
## [15] "PB" "PE" "PI" "PR" "RJ" "RN" "RO" "RR" "RS" "SC" "SE" "SP" "TO"
#New dataframe with only SP data
spcorona <- coronadata[which(coronadata$SG_UF_NOT=='SP'),]
#merge coronavirus data with sp map
names(sp)
## [1] "code_muni" "name_muni" "code_state" "abbrev_state"
## [5] "geom"
names(spcorona)
## [1] "DT_NOTIFIC" "SEM_NOT" "DT_SIN_PRI" "SEM_PRI" "SG_UF_NOT"
## [6] "ID_REGIONA" "CO_REGIONA" "ID_MUNICIP" "CO_MUN_NOT" "ID_UNIDADE"
## [11] "CO_UNI_NOT" "CS_SEXO" "DT_NASC" "NU_IDADE_N" "TP_IDADE"
## [16] "COD_IDADE" "CS_GESTANT" "CS_RACA" "CS_ETINIA" "CS_ESCOL_N"
## [21] "ID_PAIS" "CO_PAIS" "SG_UF" "ID_RG_RESI" "CO_RG_RESI"
## [26] "ID_MN_RESI" "CO_MUN_RES" "CS_ZONA" "SURTO_SG" "NOSOCOMIAL"
## [31] "AVE_SUINO" "FEBRE" "TOSSE" "GARGANTA" "DISPNEIA"
## [36] "DESC_RESP" "SATURACAO" "DIARREIA" "VOMITO" "OUTRO_SIN"
## [41] "OUTRO_DES" "PUERPERA" "FATOR_RISC" "CARDIOPATI" "HEMATOLOGI"
## [46] "SIND_DOWN" "HEPATICA" "ASMA" "DIABETES" "NEUROLOGIC"
## [51] "PNEUMOPATI" "IMUNODEPRE" "RENAL" "OBESIDADE" "OBES_IMC"
## [56] "OUT_MORBI" "MORB_DESC" "VACINA" "DT_UT_DOSE" "MAE_VAC"
## [61] "DT_VAC_MAE" "M_AMAMENTA" "DT_DOSEUNI" "DT_1_DOSE" "DT_2_DOSE"
## [66] "ANTIVIRAL" "TP_ANTIVIR" "OUT_ANTIV" "DT_ANTIVIR" "HOSPITAL"
## [71] "DT_INTERNA" "SG_UF_INTE" "ID_RG_INTE" "CO_RG_INTE" "ID_MN_INTE"
## [76] "CO_MU_INTE" "UTI" "DT_ENTUTI" "DT_SAIDUTI" "SUPORT_VEN"
## [81] "RAIOX_RES" "RAIOX_OUT" "DT_RAIOX" "AMOSTRA" "DT_COLETA"
## [86] "TP_AMOSTRA" "OUT_AMOST" "PCR_RESUL" "DT_PCR" "POS_PCRFLU"
## [91] "TP_FLU_PCR" "PCR_FLUASU" "FLUASU_OUT" "PCR_FLUBLI" "FLUBLI_OUT"
## [96] "POS_PCROUT" "PCR_VSR" "PCR_PARA1" "PCR_PARA2" "PCR_PARA3"
## [101] "PCR_PARA4" "PCR_ADENO" "PCR_METAP" "PCR_BOCA" "PCR_RINO"
## [106] "PCR_OUTRO" "DS_PCR_OUT" "CLASSI_FIN" "CLASSI_OUT" "CRITERIO"
## [111] "EVOLUCAO" "DT_EVOLUCA" "DT_ENCERRA" "DT_DIGITA" "HISTO_VGM"
## [116] "PAIS_VGM" "CO_PS_VGM" "LO_PS_VGM" "DT_VGM" "DT_RT_VGM"
## [121] "PCR_SARS2" "PAC_COCBO" "PAC_DSCBO" "OUT_ANIM" "DOR_ABD"
## [126] "FADIGA" "PERD_OLFT" "PERD_PALA" "TOMO_RES" "TOMO_OUT"
## [131] "DT_TOMO" "TP_TES_AN" "DT_RES_AN" "RES_AN" "POS_AN_FLU"
## [136] "TP_FLU_AN" "POS_AN_OUT" "AN_SARS2" "AN_VSR" "AN_PARA1"
## [141] "AN_PARA2" "AN_PARA3" "AN_ADENO" "AN_OUTRO" "DS_AN_OUT"
## [146] "TP_AM_SOR" "SOR_OUT" "DT_CO_SOR" "TP_SOR" "OUT_SOR"
## [151] "DT_RES" "RES_IGG" "RES_IGM" "RES_IGA"
names(spcorona)[names(spcorona) == "CO_MUN_NOT"] <- "code_muni"
sp_corona <- merge(sp, spcorona, by="code_muni", all=TRUE)
#Edit code_muni in sp to display only 6 digits (instead of 7)
sp$code_muni <- as.numeric(substr(sp$code_muni, 1, 6))
#How many contaminations for each city?
contam_sp <- spcorona %>%
group_by(code_muni) %>%
summarise(no_rows = length(code_muni))
# 1. Insert it into SP state map
sp_coronamap <- merge(sp,contam_sp, by="code_muni", all=TRUE)
#2. Merge pop data with other data GOOD ONE
sp_coronamap_pop <- merge(sp_coronamap,pop, by="name_muni", all=TRUE)
Iterative maps
nclr <- 7
plotclr <- brewer.pal(nclr,"Reds")
sp_coronamap_pop[["no_rows"]][is.na(sp_coronamap_pop[["no_rows"]])] <- 0
str(sp_coronamap_pop)
## Classes 'sf' and 'data.frame': 645 obs. of 10 variables:
## $ name_muni : Factor w/ 645 levels "Adamantina","Adolfo",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ code_muni : num 350010 350020 350030 350040 350050 ...
## $ code_state : Factor w/ 1 level "35": 1 1 1 1 1 1 1 1 1 1 ...
## $ abbrev_state: Factor w/ 1 level "SP": 1 1 1 1 1 1 1 1 1 1 ...
## $ no_rows : num 112 1 10 0 42 0 1 98 0 0 ...
## $ UF : Factor w/ 1 level "SP": 1 1 1 1 1 1 1 1 1 1 ...
## $ COD..UF : int 35 35 35 35 35 35 35 35 35 35 ...
## $ COD..MUNIC : int 105 204 303 402 501 550 600 709 758 808 ...
## $ pop : int 35048 3623 34863 8025 18313 5944 3139 36524 5560 4101 ...
## $ geometry :sfc_MULTIPOLYGON of length 645; first list element: List of 1
## ..$ :List of 1
## .. ..$ : num [1:185, 1:2] -51.1 -51.1 -51.1 -51.1 -51.1 ...
## ..- attr(*, "class")= chr "XY" "MULTIPOLYGON" "sfg"
## - attr(*, "sf_column")= chr "geometry"
## - attr(*, "agr")= Factor w/ 3 levels "constant","aggregate",..: NA NA NA NA NA NA NA NA NA
## ..- attr(*, "names")= chr "name_muni" "code_muni" "code_state" "abbrev_state" ...
sp_coronamap_pop[["pop"]][is.na(sp_coronamap_pop[["pop"]])] <- 0
CASES <- sp_coronamap_pop$no_rows
POP2015 <- sp_coronamap_pop$pop
sp_coronamap_pop$var <- ((CASES/POP2015))*100
class_casos <- classIntervals(sp_coronamap_pop$var, nclr, style="sd")
colcode_casos <- findColours(class_casos, plotclr)
ggplot(data=sp_coronamap_pop) +
geom_sf(size=.15, color=NA) + geom_sf(aes(fill=var)) +
scale_fill_viridis_c(direction = -1, limits = c(0, 1.5)) + #escala de cores
labs(subtitle="São Paulo", size=8, fill="Casos confirmados \na cada 100 hab") +
theme_void() +
no_axis
#How many corona deaths for each city?
spcorona_deaths <- spcorona[spcorona$EVOLUCAO %in% c("2"),]
deaths_sp <- spcorona_deaths %>%
group_by(code_muni) %>%
summarise(deaths = length(code_muni))
#Insert it in SP state map (on previous map)
sp_coronamap <- merge(sp,deaths_sp, by="code_muni", all=TRUE)
#merge pop data with other data
sp_coronamap_pop2 <- merge(sp_coronamap,pop, by="name_muni", all=TRUE)
sp_coronamap_pop2[["deaths"]][is.na(sp_coronamap_pop2[["deaths"]])] <- 0
DEATHS <- sp_coronamap_pop2$deaths
POP2015 <- sp_coronamap_pop2$pop
sp_coronamap_pop2$var_d <- ((DEATHS/POP2015))*10000 #a cada 10 mil hab
class_deaths <- classIntervals(sp_coronamap_pop2$var_d, nclr, style="sd")
colcode_deaths <- findColours(class_deaths, plotclr)
ggplot(data=sp_coronamap_pop2) +
geom_sf(size=.15, color=NA) + geom_sf(aes(fill=var_d)) +
scale_fill_viridis_c(direction = -1, limits = c(0, 35)) + #escala de cores
labs(subtitle="São Paulo", size=8, fill="Mortes confirmadas \na cada 10 mil hab") +
theme_void() +
no_axis
m <- tm_shape(sp_coronamap_pop) +
tm_polygons(col = "var",
title="Casos a cada 100 hab",
id="name_muni",
palette="BuGn")
lf <- tmap_leaflet(m)
lf
m2 <- tm_shape(sp_coronamap_pop2) +
tm_polygons(col = "var_d",
title="Mortes a cada 10 mil hab",
id="name_muni",
palette="BuGn")
lf2 <- tmap_leaflet(m2)
lf2